Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Interfacing with R via rpy2

If there is some functionality that you need and you cannot find it in a Python library, your first port of call is to check whether it’s been implemented in R. For statistical methods, R is still the most complete framework; moreover, some bioinformatics functionalities are only available in R and are probably offered as a package belonging to the Bioconductor project.

rpy2 provides a declarative interface from Python to R. As you will see, you will be able to write very elegant Python code to perform the interfacing process. To show the interface (and to try out one of the most common R data structures, the DataFrame, and one of the most popular R libraries, ggplot2), we will download its metadata from the Human 1,000 Genomes Project (http://www.1000genomes.org/). This is not a book on R, but we want to provide interesting and functional examples.

Getting ready

You will need to get the metadata file from the 1,000 Genomes sequence index...